In the field of digital multimedia communications, data streams carrying video, audio, timing and control data are packaged into various “packets”. Generally, a packet is a group of binary digits that include data and control elements which are switched and transmitted as a composite whole. The data, control elements and other information are arranged in various specific formats.
Examples of such formats are disclosed in various international Standards. These standards include, but are not limited to, the Moving Picture Experts Group Standards (e.g., MPEG-1 (11172-*), MPEG-2 (13818-*) and MPEG4 (14496-*)), H.261 and H.263. For example, MPEG defines a packet as consisting of a header followed by a number of contiguous bytes (payload) from an “elementary data stream”. An elementary stream is simply a generic term for one of the coded video, coded audio or other coded bitstreams. More specifically, an MPEG-2 “transport stream” packet comprises a header, which may be four (4) or more bytes long with a payload having a maximum length of 184 bytes. Transport stream packets are part of one or more programs that are assembled into a transport stream. The transport stream is then transmitted over a channel with a particular transfer rate.
However, transmission of packets over a noisy communication channel, e.g., wireless communication, may cause corruption in the packets received by a receiver/decoder. Furthermore, some data streams or bitstreams carry compressed data that are correlated in a manner such that partial loss of a packet may cause the receiver/decoder to discard the entire packet. Namely, compression methods are useful for representing information as accurately as possible with a minimum number of bits and thus minimizing the amount of data that must be stored or transmitted. To further increase compression efficiency, some compression methods employ “significance-based” information, e.g., a significance map-value model, to indicate to a receiver/decoder the significance of the transmitted information or absence of transmitted information. The “significance-based” information is often previously defined, e.g., using symbols, such that the receiver/decoder is able to decipher additional information from the transmitted information. However, the loss of compressed data such as “significance-based” information often results in substantial errors when a receiver/decoder attempts to decompress or decode the corrupted data.
Additionally, another compression techniques involves the transformation of an input image into transform coefficients using hierarchical subband decomposition. For example, a useful compression technique appears in the Proceedings of the International Conference on Acoustics, Speech and Signal Processing, San Francisco, Cal. March 1992, volume IV, pages 657-660, where there is disclosed a signal compression system which applies a hierarchical subband decomposition, or wavelet transform, followed by the hierarchical successive approximation entropy-coded quantizer. A wavelet pyramid, also known as critically sampled quadrature-mirror filter (QMF) subband representation, is a specific type of multiresolution hierarchical subband representation of an image.
More specifically, in a hierarchical subband system, with the exception of the highest frequency subbands, every coefficient at a given scale can be related to a set of coefficients at the next finer scale of similar orientation according to a structure called a wavelet tree. The coefficients at the coarsest scale will be called the parent nodes, and all coefficients corresponding to the same spatial or temporal location at the next finer scale of similar orientation will be called child nodes.
A typical method of coding these transform coefficients is in “tree depth scan order as shown in FIG. 1, where an image is decomposed into three levels of resolution. Specifically, the wavelet coefficients are coded in tree blocks fashion, where each tree block is represented by three separate “texture units” shown with different shadings. Each texture unit is representative of a tree structure starting from the lowest or coarsest AC band to the highest or finest AC band coefficients. However, as the image size is increased, each texture unit will encompass a greater number of transform coefficients such that each texture unit is coded using more than one packet. This can cause more information loss if error occurs in one of these packets.
Namely, the loss of a portion of a texture unit, will often cause a significant error or loss of data. Therefore, there is a need in the art for an apparatus and method for formulating a data structure or coding unit, e.g., a new texture unit, to packetize such transform coefficients to improve error resilience, regardless of the packet protocol that is employed.